1. Field of the Invention
This invention relates generally to an industrial process, and, more particularly, to applying a self-adaptive filter to a drifting industrial process, such as a semiconductor fabrication process.
2. Description of the Related Art
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group of wafers, sometimes referred to as a “lot,” using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial improvements.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various processing tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an Advanced Process Control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process, various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, particle contamination, film optical properties, film thickness, film uniformity, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools, etc. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology data to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
Run-to-run control in semiconductor manufacturing is a type of batch control, where a batch may be as small as one wafer or as large as several lots of wafers. The standard output of a run-to-run controller is a process recipe. This recipe defines the set points for “low-level” controllers built into the processing tool. In this way, the run-to-run controller supervises the tool controller by specifying required values for process variables such as temperature, pressure, flow, and process time. The tool controller initiates the activities necessary to maintain these variables at the requested values. A typical run-to-run control setup includes a feedback loop where adjustments are made to the recipe parameters based on batch properties measured after processing. Typically, the job of the run-to-run controller is to ensure that each batch hits its inline target values. Inline targets refer to measurements that are taken while the wafers have only completed some of their processing steps. The inline targets are designed to provide guidelines for the functional parts at the end of the manufacturing line.
Because the process states and other variables in the manufacturing processes can change over time, a successful controller must adapt to changing process conditions. At the foundation of such an adaptive controller are system identification techniques that aim to determine a model with the same input-output characteristics and possibly the same natural model structure as the physical system under study. In many practical applications, it is not feasible to obtain an exact model form for the process under study. Thus, online system identification often takes the form of a parameter estimation problem. In this formulation, a form for the model is predetermined, and the model parameters are updated recursively from process data. Changing process conditions can be seen as a change in the estimated model parameters over time.
To achieve adequate performance in an uncertain environment, the control system should react quickly to process changes. Adaptive control techniques are a class of control schemes where the controller automatically adjusts its model parameters and tunes to account for observed changes in the process itself. These techniques often rely on online model parameter estimation, and the controller settings are continually adjusted to match the current system model derived from the measurements.
Online system identification techniques are active as the process under study is running. They use process measurements and recursively update a system model of predetermined form. The estimator observes the system and adjusts the model parameters within the chosen model structure. In general, the estimator does not have a complete set of data with which to work. It only has access to the measurements that have already been made.
A common exponentially weighted moving average (EWMA) filtering technique can be used in recursive parameter estimation. Here, a new parameter estimate is obtained by using a weighted combination of a parameter estimate based on the current measurement and the current parameter estimate as shown:{tilde over (x)}k+1=λxk+(1−λ){tilde over (x)}k,  (1)
where x is the measured value, {tilde over (x)} is the estimate, and λ is the exponential weighting factor. While the EWMA filtering technique is generally effective, it may not be particularly effective in estimating the true process state because of the presence of drifting disturbances and non-linear, changing slope. The EWMA filtering technique is slow to react to a fast drifting process in order to filter white noise (for example, when λ<0.5).
Aside from the EWMA filtering techniques, Kalman filters can also be used in recursive parameter estimation. While Kalman filters are generally useful in drifting processes, they are not generally effective when non-white noise is present (e.g., a change in the slope of the drift).
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.